{"id":"W2088713545","doi":"10.1073/pnas.1420903112","title":"Chemodetection in fluctuating environments: Receptor coupling, buffering, and antagonism","year":2015,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Human Frontier Science Program; Simons Foundation","keywords":"Antagonism; Receptor; Computational biology; Biology; Coupling (piping); Immune system; Ligand (biochemistry); Biological system; Chemistry; Ecology; Computer science; Cell biology; Immunology; Biochemistry; Materials science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001440568,0.0005818923,0.0009581604,0.0004725104,0.0004841298,0.001337611,0.001541042,0.000904442,0.0006566719],"category_scores_gemma":[0.003593739,0.0004240608,0.0008507825,0.0004703177,0.002126912,0.001783784,0.001522827,0.001084784,0.0001186451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001048994,"about_ca_system_score_gemma":0.0008082541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001300892,"about_ca_topic_score_gemma":0.001003346,"domain_scores_codex":[0.9993324,0.0002280617,0.00002956517,0.0001474027,0.0001478115,0.0001147172],"domain_scores_gemma":[0.998395,0.001064801,0.0002634035,0.0001053281,0.00007297604,0.00009842415],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001898332,0.0001164841,0.002309578,0.0001369917,0.00007567676,0.0005738109,0.0001868442,0.6071482,0.08355872,0.2880915,0.0004444166,0.01716793],"study_design_scores_gemma":[0.00001683543,0.00005125555,0.0003775041,0.000004846795,0.00001960539,0.00008120435,0.0000260781,0.9255549,0.005232672,0.06832732,0.0002840331,0.00002371451],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1743738,0.0004382547,0.8213381,0.0005135832,0.00003999317,0.00004574181,0.00004943134,0.0001370535,0.003064195],"genre_scores_gemma":[0.9524102,0.0004342753,0.04550532,0.0002205275,0.00004147167,0.00009871164,0.00003289668,0.00003440031,0.001222178],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001541042,"threshold_uncertainty_score":0.007618546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05715096600561997,"score_gpt":0.3209870794408756,"score_spread":0.2638361134352556,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}